MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions.

Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed.

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry.

Atomic structural changes in the formation of transition metal tungstates: the role of polyoxometalate structures in material crystallization.

Material nucleation processes are poorly understood; nevertheless, an atomistic understanding of material formation would aid in the design of material synthesis methods. Here, we apply X-ray total scattering experiments with pair distribution function (PDF) analysis to study the hydrothermal synthesis of wolframite-type MWO (M : Mn, Fe, Co, Ni). The data obtained allow the mapping of the material formation pathway in detail.

DeepStruc: towards structure solution from pair distribution function data using deep generative models.

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution.

Chelating chloride using binuclear lanthanide complexes in water.

Halide recognition by supramolecular receptors and coordination complexes in water is a long-standing challenge. In this work, we report chloride binding in water and in competing media by pre-organised binuclear kinetically inert lanthanide complexes, bridged by flexible -(CH)- and -(CH)- spacers, forming [Ln(DO3A)C-2] and [Ln(DO3A)C-3], respectively. These hydrophilic, neutral lanthanide coordination complexes are shown to bind chloride with apparent association constants of up to 10 M in water and in buffered systems.

Impact of the gate geometry on adiabatic charge pumping in InAs double quantum dots.

We compare the adiabatic quantized charge pumping performed in two types of InAs nanowire double quantum dots (DQDs), either with tunnel barriers defined by closely spaced narrow bottom gates, or by well-separated side gates. In the device with an array of bottom gates of 100 nm pitch and 10 μm lengths, the pump current is quantized only up to frequencies of a few MHz due to the strong capacitive coupling between the bottom gates. In contrast, in devices with well-separated side gates with reduced mutual gate capacitances, we find well-defined pump currents up to 30 MHz.

There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis.

The development of new functional materials builds on an understanding of the intricate relationship between material structure and properties, and structural characterization is a crucial part of materials chemistry. However, elucidating the atomic structure of nanomaterials remains a challenge using conventional diffraction techniques due to the lack of long-range atomic order. Over the past decade, Pair Distribution Function (PDF) analysis of X-ray or neutron total scattering data has become a mature and well-established method capable of giving insight into the atomic structure in nanomaterials.

Inferring Leading Interactions in the p53/Mdm2/Mdmx Circuit through Live-Cell Imaging and Modeling.

The tumor-suppressive transcription factor p53 is a master regulator of stress responses. In non-stressed conditions, p53 is maintained at low levels by the ubiquitin ligase Mdm2 and its binding partner Mdmx. Mdmx depletion leads to a biphasic p53 response, with an initial post-mitotic pulse followed by oscillations.

Multifunctional Clickable Reagents for Rapid Bioorthogonal Astatination and Radio-Crosslinking.

In the past decade, several developments have expanded the chemical toolbox for astatination and the preparation of At-labeled radiopharmaceuticals. However, there is still a need for advanced methods for the synthesis of astatinated (bio)molecules to address challenges such as limited in vivo stability. Herein, we report the development of multifunctional At-labeled reagents that can be prepared by applying a modular and versatile click approach for rapid assembly.

Electronic monitoring of patient adherence to oral antihypertensive medical treatment: a systematic review.

Poor patient adherence is often the reason for suboptimal blood pressure control. Electronic monitoring is one method of assessing adherence. The aim was to systematically review the literature on electronic monitoring of patient adherence to self-administered oral antihypertensive medications.